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Self-Instantiated Recurrent Units with Dynamic Soft Recursion (Self-IRU)

This repository contains the PyTorch implementation of the Self-IRU model in the paper Self-Instantiated Recurrent Units with Dynamic Soft Recursion at NeurIPS 2021.

Installation

One needs to install the following libraries

Usage

The usage of this repository follows the TCN repository (e.g., for polyphonic music tasks). To run the Self-IRU model, set model = RNNModel(input_size, args.nhid, dropout=dropout, rnn_type='INFINITY', args=args) in the [TASK_NAME]_test.py file, where INFINITY is the alias of the Self-IRU in our implementation. If you encounter ModuleNotFoundError, try export PYTHONPATH="${PYTHONPATH}:.".

Citation

If you find this repository helpful, please cite our paper:

@article{zhang2021selfiru,
    title={Self-Instantiated Recurrent Units with Dynamic Soft Recursion
},
    author={Zhang, Aston and Tay, Yi and Shen, Yikang and Chan, Alvin and Zhang, Shuai},
    booktitle={Advances in neural information processing systems},
    year={2021}
}

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Repo for the Self-IRU paper

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